{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torchtext'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-d4440aaee52d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunctional\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchtext\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorchtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchtext\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchtext'"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from collections import Counter\n",
    "import string\n",
    "import re\n",
    "import argparse\n",
    "import sys\n",
    "import argparse\n",
    "import copy, json, os\n",
    "from time import gmtime, strftime\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "import tensorflow as tf\n",
    "from torchtext import data\n",
    "import torchtext\n",
    "from torchtext import datasets\n",
    "from torchtext.vocab import GloVe\n",
    "import nltk\n",
    "from tensorboardX import SummaryWriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、定义初始变量参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_args():\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument('--char-dim', default=8, type=int)\n",
    "    # char-dim 默认值是8\n",
    "    parser.add_argument('--char-channel-width', default=5, type=int)\n",
    "    # char-channel-width 默认值是5 以下类似\n",
    "    parser.add_argument('--char-channel-size', default=100, type=int)\n",
    "    parser.add_argument('--context-threshold', default=400, type=int)\n",
    "    parser.add_argument('--dev-batch-size', default=100, type=int)\n",
    "    parser.add_argument('--dev-file', default='dev-v1.1.json')\n",
    "    parser.add_argument('--dropout', default=0.2, type=float)\n",
    "    parser.add_argument('--epoch', default=12, type=int)\n",
    "    parser.add_argument('--exp-decay-rate', default=0.999, type=float)\n",
    "    parser.add_argument('--gpu', default=0, type=int)\n",
    "    parser.add_argument('--hidden-size', default=100, type=int)\n",
    "    parser.add_argument('--learning-rate', default=0.5, type=float)\n",
    "    parser.add_argument('--print-freq', default=250, type=int)\n",
    "    parser.add_argument('--train-batch-size', default=60, type=int)\n",
    "    parser.add_argument('--train-file', default='train-v1.1.json')\n",
    "    parser.add_argument('--word-dim', default=100, type=int)\n",
    "    args = parser.parse_args(args=[]) \n",
    "    return args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = parse_args()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、SQuAD问答数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、查看数据集结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SQuAD问答数据介绍：https://rajpurkar.github.io/SQuAD-explorer/\n",
    "这个数据集有两个文件，验证集和测试集：train-v1.1.json，dev-v1.1.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/squad/dev-v1.1.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-8ce5f71c559a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/squad/dev-v1.1.json'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                 \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 该方法每次读出一行内容\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/squad/dev-v1.1.json'"
     ]
    }
   ],
   "source": [
    "with open('data/squad/dev-v1.1.json', 'r', encoding='utf-8') as f:\n",
    "        try:\n",
    "            while True:\n",
    "                line = f.readline() # 该方法每次读出一行内容\n",
    "                if line:\n",
    "                    print(\"type(line)\",type(line)) # 直接打印就是字符串格式\n",
    "                    r = json.loads(line)\n",
    "                    print(\"type(r)\",type(r)) # 使用json.loads将字符串转化为字典\n",
    "                    print(r)\n",
    "                else:\n",
    "                    break\n",
    "        except:\n",
    "            f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-9-91763e591b63>, line 38)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-9-91763e591b63>\"\u001b[1;36m, line \u001b[1;32m38\u001b[0m\n\u001b[1;33m    },\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# 数据架构如下\n",
    "{\n",
    "    \"data\": [\n",
    "        {\n",
    "            \"title\": \"Super_Bowl_50\", # 第一个主题\n",
    "            \"paragraphs\": [\n",
    "                {\n",
    "                    \"context\": \" numerals 50.......\", # 每个主题会有很多context短文,这里只列出一个\n",
    "                    \"qas\": [  # 这个列表里放问题和答案的位置，每篇context会有很有很多answer和question，这里只列出一个\n",
    "                        {\n",
    "                            \"answers\": [  # 一个问题会有三个答案，三个答案都是对的，只是在context不同或相同位置\n",
    "                                {         # 下面三个答案都在相同的位置\n",
    "                                    \"answer_start\": 177,  # 答案在文中的起始位置是第177的字符。\n",
    "                                    \"text\": \"Denver Broncos\"\n",
    "                                },\n",
    "                                {\n",
    "                                    \"answer_start\": 177,\n",
    "                                    \"text\": \"Denver Broncos\"\n",
    "                                },\n",
    "                                {\n",
    "                                    \"answer_start\": 177,\n",
    "                                    \"text\": \"Denver Broncos\"\n",
    "                                }\n",
    "                            ],\n",
    "                            \"question\": \"Which NFL team represented the AFC at Super Bowl 50?\",\n",
    "                            \"id\": \"56be4db0acb8001400a502ec\"\n",
    "                        }\n",
    "\n",
    "                    ]\n",
    "                }\n",
    "                \n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        {\n",
    "            \"title\": \"Warsaw\", # 第二个主题\n",
    "            \"paragraphs\":   \n",
    "        },\n",
    "        \n",
    "        {\n",
    "            \"title\": \"Normans\", # 第三个主题\n",
    "            \"paragraphs\": \n",
    "        },\n",
    "        \n",
    "        {\n",
    "            \"title\": \"Nikola_Tesla\", # 第四个主题\n",
    "            \"paragraphs\": \n",
    "        },\n",
    "        ........... # 还有很多\n",
    "        \n",
    "    ],\n",
    "    \"version\": \"1.1\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、定义分词方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_tokenize(tokens):\n",
    "    tokens = [token.replace(\"''\", '\"').replace(\"``\", '\"') for token in nltk.word_tokenize(tokens)]\n",
    "    # nltk.word_tokenize(tokens)分词，replace规范化引号，方便后面处理\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、清洗数据，并生成数据迭代器[](http://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SQuAD():\n",
    "    def __init__(self, args):\n",
    "\n",
    "        # 以下定好中间输出缓存文件的路径\n",
    "        path = 'data/squad' \n",
    "        dataset_path = path + '/torch_text/' \n",
    "        train_examples_path = dataset_path + 'train_examples.pt'\n",
    "        dev_examples_path = dataset_path + 'dev_examples.pt'\n",
    "\n",
    "        print(\"preprocessing data files...\")\n",
    "        if not os.path.exists(f'{path}/{args.train_file}l'):\n",
    "            # 字符串前以f开头表示在字符串内支持大括号内的 python 表达式\n",
    "            # args.train_file = 'train-v1.1.json'\n",
    "            print(f'{path}/{args.train_file}')\n",
    "            self.preprocess_file(f'{path}/{args.train_file}')  \n",
    "            # preprocess_file下面函数有定义，完成文件的预处理\n",
    "        if not os.path.exists(f'{path}/{args.dev_file}l'):\n",
    "            # args.dev_file = 'dev-v1.1.json'\n",
    "            self.preprocess_file(f'{path}/{args.dev_file}')\n",
    "            \n",
    "        # 下面是用torchtext处理数据的步骤看不懂了，有知道的可以交流下   \n",
    "        self.RAW = data.RawField()# 这个是完全空白的field，意味着不经过任何处理\n",
    "        # explicit declaration for torchtext compatibility\n",
    "        self.RAW.is_target = False\n",
    "        self.CHAR_NESTING = data.Field(batch_first=True, tokenize=list, lower=True)\n",
    "        self.CHAR = data.NestedField(self.CHAR_NESTING, tokenize=word_tokenize)\n",
    "        self.WORD = data.Field(batch_first=True, tokenize=word_tokenize, lower=True, include_lengths=True)\n",
    "        self.LABEL = data.Field(sequential=False, unk_token=None, use_vocab=False)\n",
    "\n",
    "        dict_fields = {'id': ('id', self.RAW),\n",
    "                       's_idx': ('s_idx', self.LABEL),\n",
    "                       'e_idx': ('e_idx', self.LABEL),\n",
    "                       'context': [('c_word', self.WORD), ('c_char', self.CHAR)],\n",
    "                       'question': [('q_word', self.WORD), ('q_char', self.CHAR)]}\n",
    "\n",
    "        list_fields = [('id', self.RAW), ('s_idx', self.LABEL), ('e_idx', self.LABEL),\n",
    "                       ('c_word', self.WORD), ('c_char', self.CHAR),\n",
    "                       ('q_word', self.WORD), ('q_char', self.CHAR)]\n",
    "\n",
    "        if os.path.exists(dataset_path):\n",
    "            print(\"loading splits...\")\n",
    "            train_examples = torch.load(train_examples_path)\n",
    "            dev_examples = torch.load(dev_examples_path)\n",
    "\n",
    "            self.train = data.Dataset(examples=train_examples, fields=list_fields)\n",
    "            self.dev = data.Dataset(examples=dev_examples, fields=list_fields)\n",
    "        else:\n",
    "            print(\"building splits...\")\n",
    "             # 划分训练集和验证集\n",
    "            self.train, self.dev = data.TabularDataset.splits(\n",
    "                path=path,\n",
    "                train=f'{args.train_file}l',\n",
    "                validation=f'{args.dev_file}l',\n",
    "                format='json',\n",
    "                fields=dict_fields)\n",
    "\n",
    "            os.makedirs(dataset_path)\n",
    "            torch.save(self.train.examples, train_examples_path)\n",
    "            torch.save(self.dev.examples, dev_examples_path)\n",
    "\n",
    "        #cut too long context in the training set for efficiency.\n",
    "        if args.context_threshold > 0:\n",
    "            self.train.examples = [e for e in self.train.examples if len(e.c_word) <= args.context_threshold]\n",
    "\n",
    "        print(\"building vocab...\")\n",
    "        self.CHAR.build_vocab(self.train, self.dev) # 字符向量没有设置vector\n",
    "        self.WORD.build_vocab(self.train, self.dev, vectors=GloVe(name='6B', dim=args.word_dim))\n",
    "        # 加载Glove向量，args.word_dim = 100\n",
    "\n",
    "        print(\"building iterators...\")\n",
    "        device = torch.device(f\"cuda:{args.gpu}\" if torch.cuda.is_available() else \"cpu\")\n",
    "        # 生成迭代器\n",
    "        self.train_iter, self.dev_iter = \\\n",
    "            data.BucketIterator.splits((self.train, self.dev),\n",
    "                                       batch_sizes=[args.train_batch_size, args.dev_batch_size],\n",
    "                                       device=device,\n",
    "                                       sort_key=lambda x: len(x.c_word))\n",
    "\n",
    "    def preprocess_file(self,path):\n",
    "        dump = []\n",
    "        abnormals = [' ', '\\n', '\\u3000', '\\u202f', '\\u2009']\n",
    "        # 空白无效字符列表\n",
    "\n",
    "        with open(path, 'r', encoding='utf-8') as f:\n",
    "            data = json.load(f) # 直接文件句柄转化为字典\n",
    "            data = data['data'] # 返回值data是个列表，字典是列表的元素\n",
    "\n",
    "            for article in data:\n",
    "                # 每个article是一个字典，一个字典包含一个title的信息\n",
    "                for paragraph in article['paragraphs']:\n",
    "                    # 每个paragraph是一个字典，一个字典里有一个context和qas的信息，qas是问题和答案。\n",
    "                    context = paragraph['context']\n",
    "                    # context的内容，是字符串，如：\" numerals 50.............\"\n",
    "                    tokens = word_tokenize(context) # 对context进行分词\n",
    "                    for qa in paragraph['qas']:\n",
    "                        # 每个qa是一个字典，一个字典包含一对answers和question的信息\n",
    "                        id = qa['id']\n",
    "                        # 取出这对answers和question的id信息，如：\"56be4db0acb8001400a502ec\"\n",
    "                        question = qa['question']\n",
    "                        # 取出question，如：\"Which NFL team represented the AFC at Super Bowl 50?\"\n",
    "                        for ans in qa['answers']:\n",
    "                            # ans为每个答案，共有三个标准答案，可以相同，可以不同，统一为3个。\n",
    "                            answer = ans['text']\n",
    "                            # 问题的每个回答，如：\"Denver Broncos\"\n",
    "                            s_idx = ans['answer_start']\n",
    "                            # 每个回答的start位置，数值代表context中第几个字符，如：177\n",
    "                            e_idx = s_idx + len(answer)\n",
    "                            # 每个回答的end位置\n",
    "\n",
    "\n",
    "                            # 下面重新更新字符的起始位置，使用字符计算位置改为使用单词计算位置\n",
    "                            # 请看下面单元格的示例输出有助理解。\n",
    "                            l = 0\n",
    "                            s_found = False\n",
    "                            for i, t in enumerate(tokens):\n",
    "                                # 循环t次，t为分词后的单词数量\n",
    "                                while l < len(context):\n",
    "                                    if context[l] in abnormals:\n",
    "                                        # context中有空白无效字符，就计数\n",
    "                                        l += 1\n",
    "                                    else:    # 一碰到不是空白字符的就break\n",
    "                                        break\n",
    "                                # exceptional cases\n",
    "                                if t[0] == '\"' and context[l:l + 2] == '\\'\\'':\n",
    "                                    # 专门计算context=''an 这种长度，这个长度为4\n",
    "                                    t = '\\'\\'' + t[1:] \n",
    "                                elif t == '\"' and context[l:l + 2] == '\\'\\'':\n",
    "                                    # 专门计算context='' 这种长度\n",
    "                                    # 上面t[0] == '\"'表达式包含了这种，所以我认为这个表达式没用上\n",
    "                                    t = '\\'\\''\n",
    "\n",
    "                                l += len(t)\n",
    "                                if l > s_idx and s_found == False:\n",
    "                                    # 只要计数超过起始位置值，这个单词就是start的单词\n",
    "                                    s_idx = i\n",
    "                                    s_found = True\n",
    "                                if l >= e_idx:\n",
    "                                    # 这里不出错的话，等于e_idx就是end的单词\n",
    "                                    e_idx = i\n",
    "                                    break\n",
    "\n",
    "                            # 这里把三个answer分开，每个answer都放进字典中,并作为一个样本\n",
    "                            dump.append(dict([('id', id),\n",
    "                                              ('context', context),\n",
    "                                              ('question', question),\n",
    "                                              ('answer', answer),\n",
    "                                              ('s_idx', s_idx),\n",
    "                                              ('e_idx', e_idx)]))\n",
    "        with open(f'{path}l', 'w', encoding='utf-8') as f:\n",
    "            for line in dump:\n",
    "                # line为字典，一个样本存储\n",
    "                json.dump(line, f)\n",
    "                #dump：将dict类型转换为json字符串格式，写入到文件\n",
    "                print('', file=f) # 这里print的作用就是换行用的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preprocessing data files...\n",
      "data/squad/train-v1.1.json\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/squad/train-v1.1.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-baf688428b65>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSQuAD\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-11-50aa82bf15a7>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[1;31m# args.train_file = 'train-v1.1.json'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{path}/{args.train_file}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocess_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{path}/{args.train_file}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m             \u001b[1;31m# preprocess_file下面函数有定义，完成文件的预处理\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{path}/{args.dev_file}l'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-50aa82bf15a7>\u001b[0m in \u001b[0;36mpreprocess_file\u001b[1;34m(self, path)\u001b[0m\n\u001b[0;32m     82\u001b[0m         \u001b[1;31m# 空白无效字符列表\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 直接文件句柄转化为字典\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# 返回值data是个列表，字典是列表的元素\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/squad/train-v1.1.json'"
     ]
    }
   ],
   "source": [
    "data = SQuAD(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 上面不太明白的举例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Super', 'Bowl', '50', 'was', '``', 'an', \"''\", 'American', 'football', 'game', 'to', 'determine', 'the', 'champion', 'of', 'the', 'National', 'Football', 'League', '(', 'NFL', ')', 'for', 'the', '2015', 'season', '.', 'The', 'American', 'Football', 'Conference', '(', 'AFC', ')', 'champion', 'Denver', 'Broncos', 'defeated', 'the', 'National', 'Football', 'Conference', '(', 'NFC', ')', 'champion', 'Carolina', 'Panthers', '24–10', 'to', 'earn', 'their', 'third', 'Super', 'Bowl', 'title', '.', 'The', 'game', 'was', 'played', 'on', 'February', '7', ',', '2016', ',', 'at', 'Levi', \"'s\", 'Stadium', 'in', 'the', 'San', 'Francisco', 'Bay', 'Area', 'at', 'Santa', 'Clara', ',', 'California', '.', 'As', 'this', 'was', 'the', '50th', 'Super', 'Bowl', ',', 'the', 'league', 'emphasized', 'the', '``', 'golden', 'anniversary', \"''\", 'with', 'various', 'gold-themed', 'initiatives', ',', 'as', 'well', 'as', 'temporarily', 'suspending', 'the', 'tradition', 'of', 'naming', 'each', 'Super', 'Bowl', 'game', 'with', 'Roman', 'numerals', '(', 'under', 'which', 'the', 'game', 'would', 'have', 'been', 'known', 'as', '``', 'Super', 'Bowl', 'L', \"''\", ')', ',', 'so', 'that', 'the', 'logo', 'could', 'prominently', 'feature', 'the', 'Arabic', 'numerals', '50', '.']\n",
      "--------------------------------------------------------------------------------\n",
      "['Super', 'Bowl', '50', 'was', '\"', 'an', '\"', 'American', 'football', 'game', 'to', 'determine', 'the', 'champion', 'of', 'the', 'National', 'Football', 'League', '(', 'NFL', ')', 'for', 'the', '2015', 'season', '.', 'The', 'American', 'Football', 'Conference', '(', 'AFC', ')', 'champion', 'Denver', 'Broncos', 'defeated', 'the', 'National', 'Football', 'Conference', '(', 'NFC', ')', 'champion', 'Carolina', 'Panthers', '24–10', 'to', 'earn', 'their', 'third', 'Super', 'Bowl', 'title', '.', 'The', 'game', 'was', 'played', 'on', 'February', '7', ',', '2016', ',', 'at', 'Levi', \"'s\", 'Stadium', 'in', 'the', 'San', 'Francisco', 'Bay', 'Area', 'at', 'Santa', 'Clara', ',', 'California', '.', 'As', 'this', 'was', 'the', '50th', 'Super', 'Bowl', ',', 'the', 'league', 'emphasized', 'the', '\"', 'golden', 'anniversary', '\"', 'with', 'various', 'gold-themed', 'initiatives', ',', 'as', 'well', 'as', 'temporarily', 'suspending', 'the', 'tradition', 'of', 'naming', 'each', 'Super', 'Bowl', 'game', 'with', 'Roman', 'numerals', '(', 'under', 'which', 'the', 'game', 'would', 'have', 'been', 'known', 'as', '\"', 'Super', 'Bowl', 'L', '\"', ')', ',', 'so', 'that', 'the', 'logo', 'could', 'prominently', 'feature', 'the', 'Arabic', 'numerals', '50', '.']\n"
     ]
    }
   ],
   "source": [
    "# 举例子\n",
    "a = \" \\u2009\\n\\u3000Super Bowl 50 was ''an'' American football     \\u3000game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24\\u201310 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \\\"golden anniversary\\\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \\\"Super Bowl L\\\"), so that the logo could prominently feature the Arabic numerals 50.\"\n",
    "tokens = word_tokenize(a)\n",
    "print(nltk.word_tokenize(a)) # 所有的“\\u2009”，“\\n”，“\\u3000”等空白字符都去掉了\n",
    "print(\"----\"*20)\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " \n",
      "\n",
      "\n",
      "　\n",
      "S\n",
      "u\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\u2009'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(a[0]) # 空白字符打印不出来\n",
    "print(a[1]) # 空白字符打印不出来\n",
    "print(a[2]) # 空白字符打印不出来\n",
    "print(a[3]) # 空白字符打印不出来\n",
    "print(a[4]) \n",
    "print(a[5])\n",
    "a[1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# 下面特别注意\n",
    "print(tokens[4][0]== '\"') # 虽然切分后看起来是\"''\"，但实际上是'\"'\n",
    "print(tokens[4][0]== \"''\") \n",
    "print(tokens[5]== '\"')\n",
    "print(len('\"')) # 这种长度为1\n",
    "print(len(\"''\")) # 这种长度为2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t= Super\n",
      "l 4\n",
      "len(t) 5\n",
      "l 9\n",
      "t= Bowl\n",
      "l 10\n",
      "len(t) 4\n",
      "l 14\n",
      "t= 50\n",
      "l 15\n",
      "len(t) 2\n",
      "l 17\n",
      "t= was\n",
      "l 18\n",
      "len(t) 3\n",
      "l 21\n",
      "t= \"\n",
      "l 22\n",
      "1111111111111111111\n",
      "\"\n",
      "\n",
      "''\n",
      "len(t) 2\n",
      "l 24\n",
      "t= an\n",
      "l 24\n",
      "len(t) 2\n",
      "l 26\n",
      "t= \"\n",
      "l 26\n",
      "1111111111111111111\n",
      "\"\n",
      "\n",
      "''\n",
      "len(t) 2\n",
      "l 28\n",
      "t= American\n",
      "l 29\n",
      "len(t) 8\n",
      "l 37\n",
      "t= football\n",
      "l 38\n",
      "len(t) 8\n",
      "l 46\n",
      "t= game\n",
      "l 52\n",
      "len(t) 4\n",
      "l 56\n",
      "t= to\n",
      "l 57\n",
      "len(t) 2\n",
      "l 59\n",
      "t= determine\n",
      "l 60\n",
      "len(t) 9\n",
      "l 69\n",
      "t= the\n",
      "l 70\n",
      "len(t) 3\n",
      "l 73\n",
      "t= champion\n",
      "l 74\n",
      "len(t) 8\n",
      "l 82\n",
      "t= of\n",
      "l 83\n",
      "len(t) 2\n",
      "l 85\n",
      "t= the\n",
      "l 86\n",
      "len(t) 3\n",
      "l 89\n",
      "t= National\n",
      "l 90\n",
      "len(t) 8\n",
      "l 98\n",
      "t= Football\n",
      "l 99\n",
      "len(t) 8\n",
      "l 107\n",
      "t= League\n",
      "l 108\n",
      "len(t) 6\n",
      "l 114\n",
      "t= (\n",
      "l 115\n",
      "len(t) 1\n",
      "l 116\n",
      "t= NFL\n",
      "l 116\n",
      "len(t) 3\n",
      "l 119\n",
      "t= )\n",
      "l 119\n",
      "len(t) 1\n",
      "l 120\n",
      "t= for\n",
      "l 121\n",
      "len(t) 3\n",
      "l 124\n",
      "t= the\n",
      "l 125\n",
      "len(t) 3\n",
      "l 128\n",
      "t= 2015\n",
      "l 129\n",
      "len(t) 4\n",
      "l 133\n",
      "t= season\n",
      "l 134\n",
      "len(t) 6\n",
      "l 140\n",
      "t= .\n",
      "l 140\n",
      "len(t) 1\n",
      "l 141\n",
      "t= The\n",
      "l 142\n",
      "len(t) 3\n",
      "l 145\n",
      "t= American\n",
      "l 146\n",
      "len(t) 8\n",
      "l 154\n",
      "t= Football\n",
      "l 155\n",
      "len(t) 8\n",
      "l 163\n",
      "t= Conference\n",
      "l 164\n",
      "len(t) 10\n",
      "l 174\n",
      "t= (\n",
      "l 175\n",
      "len(t) 1\n",
      "l 176\n",
      "t= AFC\n",
      "l 176\n",
      "len(t) 3\n",
      "l 179\n",
      "s_idx 32\n",
      "t= )\n",
      "l 179\n",
      "len(t) 1\n",
      "l 180\n",
      "t= champion\n",
      "l 181\n",
      "len(t) 8\n",
      "l 189\n",
      "t= Denver\n",
      "l 190\n",
      "len(t) 6\n",
      "l 196\n",
      "e_idx 35\n"
     ]
    }
   ],
   "source": [
    "# 查看输出理解\n",
    "s_idx = 177\n",
    "e_idx = s_idx + len(\"Denver Broncos\")\n",
    "l=0\n",
    "context = \" \\u2009\\n\\u3000Super Bowl 50 was ''an'' American football     \\u3000game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24\\u201310 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \\\"golden anniversary\\\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \\\"Super Bowl L\\\"), so that the logo could prominently feature the Arabic numerals 50.\"\n",
    "tokens = word_tokenize(context)\n",
    "abnormals = [' ', '\\n', '\\u3000', '\\u202f', '\\u2009']\n",
    "s_found = False\n",
    "for i, t in enumerate(tokens):\n",
    "    print(\"t=\",t)\n",
    "    while l < len(context):\n",
    "        if context[l] in abnormals:\n",
    "            l += 1\n",
    "        else:\n",
    "            break\n",
    "    print(\"l\",l)\n",
    "    # exceptional cases\n",
    "    if t[0] == '\"' and context[l:l + 2] == '\\'\\'':\n",
    "        print(\"1111111111111111111\")\n",
    "        print(t)\n",
    "        print(t[1:])\n",
    "        t = '\\'\\'' + t[1:]\n",
    "        print(t)\n",
    "    elif t == '\"' and context[l:l + 2] == '\\'\\'':\n",
    "        # 看输出结果，这个表达式没有用到\n",
    "        print(\"22222222222222222222\")\n",
    "        print(t)\n",
    "        t = '\\'\\''\n",
    "    print(\"len(t)\",len(t))\n",
    "    l += len(t)\n",
    "    print(\"l\",l)\n",
    "    if l > s_idx and s_found == False:\n",
    "        s_idx = i\n",
    "        print(\"s_idx\",s_idx)\n",
    "        s_found = True\n",
    "    if l >= e_idx:\n",
    "        e_idx = i\n",
    "        print(\"e_idx\",e_idx)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'torchtext.data' has no attribute 'train_iter'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-ffd59a31e61d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_iter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#一个batch的信息\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# 训练集的batch_sizes=60\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# batch.c_word = 60x293，293是60个样本中最长样本token的单词数\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# batch.c_char = 60x293x25，25是某个单词字符的最大的数量\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'torchtext.data' has no attribute 'train_iter'"
     ]
    }
   ],
   "source": [
    "batch = next(iter(data.train_iter)) #一个batch的信息\n",
    "print(batch)\n",
    "# 训练集的batch_sizes=60\n",
    "# batch.c_word = 60x293，293是60个样本中最长样本token的单词数\n",
    "# batch.c_char = 60x293x25，25是某个单词字符的最大的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(batch.q_word)\n",
    "print(batch.q_char[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下面为args新增参数，并赋值\n",
    "# hasattr() getattr() setattr() 函数使用方法详解https://www.cnblogs.com/cenyu/p/5713686.html\n",
    "setattr(args, 'char_vocab_size', len(data.CHAR.vocab)) # 设置属性args.char_vocab_size的值 = len(data.CHAR.vocab)\n",
    "setattr(args, 'word_vocab_size', len(data.WORD.vocab))\n",
    "setattr(args, 'dataset_file', f'data/squad/{args.dev_file}')\n",
    "setattr(args, 'prediction_file', f'prediction{args.gpu}.out')\n",
    "setattr(args, 'model_time', strftime('%H:%M:%S', gmtime())) # 时间\n",
    "print('data loading complete!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BIDAF"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    \n",
    "    def __init__(self, input_size, hidden_size, batch_first=False, num_layers=1, bidirectional=False, dropout=0.2):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.rnn = nn.LSTM(input_size=input_size,\n",
    "                           hidden_size=hidden_size,\n",
    "                           num_layers=num_layers,\n",
    "                           bidirectional=bidirectional,\n",
    "                           batch_first=batch_first)\n",
    "        self.reset_params() # 重置参数\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "\n",
    "    def reset_params(self):\n",
    "        for i in range(self.rnn.num_layers):\n",
    "            nn.init.orthogonal_(getattr(self.rnn, f'weight_hh_l{i}')) # hidden-hidden weights\n",
    "            # weight_hh_l{i}、weight_ih_l{i}、bias_hh_l{i}、bias_ih_l{i} 都是nn.LSTM源码里的参数\n",
    "            # getattr取出源码里参数的值，用nn.init.orthogonal_正交进行重新初始化\n",
    "            # nn.init初始化方法看这个链接：https://www.aiuai.cn/aifarm613.html\n",
    "            nn.init.kaiming_normal_(getattr(self.rnn, f'weight_ih_l{i}')) # input-hidden weights\n",
    "            nn.init.constant_(getattr(self.rnn, f'bias_hh_l{i}'), val=0) # hidden-hidden bias\n",
    "            nn.init.constant_(getattr(self.rnn, f'bias_ih_l{i}'), val=0) # input-hidden bias\n",
    "            getattr(self.rnn, f'bias_hh_l{i}').chunk(4)[1].fill_(1)\n",
    "            # .chunk看下这个链接：https://blog.csdn.net/XuM222222/article/details/92380538\n",
    "            # .fill_(1),下划线代表直接替换，看链接：https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.fill.html\n",
    "\n",
    "            if self.rnn.bidirectional: # 双向，需要初始化反向的参数\n",
    "                nn.init.orthogonal_(getattr(self.rnn, f'weight_hh_l{i}_reverse'))\n",
    "                nn.init.kaiming_normal_(getattr(self.rnn, f'weight_ih_l{i}_reverse'))\n",
    "                nn.init.constant_(getattr(self.rnn, f'bias_hh_l{i}_reverse'), val=0)\n",
    "                nn.init.constant_(getattr(self.rnn, f'bias_ih_l{i}_reverse'), val=0)\n",
    "                getattr(self.rnn, f'bias_hh_l{i}_reverse').chunk(4)[1].fill_(1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x是一个元组(c, c_lens)\n",
    "        x, x_len = x\n",
    "        # x = (batch, seq_len, hidden_size * 2)\n",
    "        # x_len = (batch) 一个batch中所有context或question的样本长度\n",
    "        x = self.dropout(x)\n",
    "        \n",
    "        # 下面一顿操作和第七课机器翻译的一样，\n",
    "        # 看下这篇博客理解：https://www.cnblogs.com/sbj123456789/p/9834018.html\n",
    "        x_len_sorted, x_idx = torch.sort(x_len, descending=True)\n",
    "        x_sorted = x.index_select(dim=0, index=x_idx)\n",
    "        _, x_ori_idx = torch.sort(x_idx)\n",
    "\n",
    "        x_packed = nn.utils.rnn.pack_padded_sequence(x_sorted, x_len_sorted, batch_first=True)\n",
    "        x_packed, (h, c) = self.rnn(x_packed)\n",
    "\n",
    "        x = nn.utils.rnn.pad_packed_sequence(x_packed, batch_first=True)[0]\n",
    "        x = x.index_select(dim=0, index=x_ori_idx)\n",
    "        h = h.permute(1, 0, 2).contiguous().view(-1, h.size(0) * h.size(2)).squeeze()\n",
    "        h = h.index_select(dim=0, index=x_ori_idx)\n",
    "        # x = (batch, seq_len, hidden_size * 2)\n",
    "        # h = (1, batch, hidden_size * 2) 这个维度不用管\n",
    "        return x, h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(nn.Module):\n",
    "    def __init__(self, in_features, out_features, dropout=0.0):\n",
    "        super(Linear, self).__init__()\n",
    "\n",
    "        self.linear = nn.Linear(in_features=in_features, out_features=out_features)\n",
    "        # in_features = hidden_size * 2\n",
    "        # out_features = hidden_size * 2\n",
    "        if dropout > 0:\n",
    "            self.dropout = nn.Dropout(p=dropout)\n",
    "        self.reset_params()\n",
    "\n",
    "    def reset_params(self):\n",
    "        nn.init.kaiming_normal_(self.linear.weight)\n",
    "        nn.init.constant_(self.linear.bias, 0)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if hasattr(self, 'dropout'): # 判断self有没有'dropout'这个参数，返回bool值\n",
    "            x = self.dropout(x)\n",
    "        x = self.linear(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "args.char_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 看英文论文或这篇博客理解模型：https://blog.csdn.net/u014665013/article/details/79793395\n",
    "class BiDAF(nn.Module):\n",
    "    def __init__(self, args, pretrained):\n",
    "        # pretrained = data.WORD.vocab.vectors = (108777, 100)\n",
    "        super(BiDAF, self).__init__()\n",
    "        self.args = args\n",
    "\n",
    "        # 1. Character Embedding Layer 是模型示意图左边的层的名字，从下往上\n",
    "        # 字符编码层\n",
    "        self.char_emb = nn.Embedding(args.char_vocab_size, args.char_dim, padding_idx=1)\n",
    "        # args.char_vocab_size = 1307，args.char_dim = 8\n",
    "        nn.init.uniform_(self.char_emb.weight, -0.001, 0.001)\n",
    "        # 初始化权重\n",
    "\n",
    "        self.char_conv = nn.Conv2d(1, args.char_channel_size, (args.char_dim, args.char_channel_width))\n",
    "        # args.char_channel_size = 100 卷积核数量 \n",
    "        # (args.char_dim, args.char_channel_width) = (8,5) 过滤器大小\n",
    "\n",
    "        # 2. Word Embedding Layer\n",
    "        # 单词编码层\n",
    "        # initialize word embedding with GloVe\n",
    "        self.word_emb = nn.Embedding.from_pretrained(pretrained, freeze=True)\n",
    "        # 初始化词向量权重，用的Glove向量 \n",
    "\n",
    "        # highway network\n",
    "        assert self.args.hidden_size * 2 == (self.args.char_channel_size + self.args.word_dim)\n",
    "        for i in range(2):\n",
    "            setattr(self, f'highway_linear{i}',\n",
    "                    nn.Sequential(Linear(args.hidden_size * 2, args.hidden_size * 2),\n",
    "                                  nn.ReLU()))\n",
    "            # 设置highway_linear0 = nn.Sequential(Linear(args.hidden_size * 2, args.hidden_size * 2)\n",
    "            # 设置highway_linear1 = nn.Sequential(Linear(args.hidden_size * 2, args.hidden_size * 2)\n",
    "            # args.hidden_size = 100\n",
    "                                \n",
    "            setattr(self, f'highway_gate{i}',\n",
    "                    nn.Sequential(Linear(args.hidden_size * 2, args.hidden_size * 2),\n",
    "                                  nn.Sigmoid()))\n",
    "\n",
    "        # 3. Contextual Embedding Layer\n",
    "        # 上下文，和答案嵌入层，用的LSTM\n",
    "        # 下面LSTM定位到了自定义的class LSTM(nn.Module)。\n",
    "        self.context_LSTM = LSTM(input_size=args.hidden_size * 2,\n",
    "                                 hidden_size=args.hidden_size,\n",
    "                                 bidirectional=True,\n",
    "                                 batch_first=True,\n",
    "                                 dropout=args.dropout) \n",
    "\n",
    "        # 4. Attention Flow Layer\n",
    "        # 注意力层\n",
    "        self.att_weight_c = Linear(args.hidden_size * 2, 1)\n",
    "        self.att_weight_q = Linear(args.hidden_size * 2, 1)\n",
    "        self.att_weight_cq = Linear(args.hidden_size * 2, 1)\n",
    "\n",
    "        # 5. Modeling Layer\n",
    "        self.modeling_LSTM1 = LSTM(input_size=args.hidden_size * 8,\n",
    "                                   hidden_size=args.hidden_size,\n",
    "                                   bidirectional=True,\n",
    "                                   batch_first=True,\n",
    "                                   dropout=args.dropout)\n",
    "\n",
    "        self.modeling_LSTM2 = LSTM(input_size=args.hidden_size * 2,\n",
    "                                   hidden_size=args.hidden_size,\n",
    "                                   bidirectional=True,\n",
    "                                   batch_first=True,\n",
    "                                   dropout=args.dropout)\n",
    "\n",
    "        # 6. Output Layer\n",
    "        self.p1_weight_g = Linear(args.hidden_size * 8, 1, dropout=args.dropout)\n",
    "        self.p1_weight_m = Linear(args.hidden_size * 2, 1, dropout=args.dropout)\n",
    "        self.p2_weight_g = Linear(args.hidden_size * 8, 1, dropout=args.dropout)\n",
    "        self.p2_weight_m = Linear(args.hidden_size * 2, 1, dropout=args.dropout)\n",
    "\n",
    "        self.output_LSTM = LSTM(input_size=args.hidden_size * 2,\n",
    "                                hidden_size=args.hidden_size,\n",
    "                                bidirectional=True,\n",
    "                                batch_first=True,\n",
    "                                dropout=args.dropout)\n",
    "\n",
    "        self.dropout = nn.Dropout(p=args.dropout)\n",
    "\n",
    "    def forward(self, batch):\n",
    "        # batch里面有'id','s_idx','e_idx', 'c_word','c_char','q_word', 'q_char'数据\n",
    "        # TODO: More memory-efficient architecture\n",
    "        def char_emb_layer(x):\n",
    "            \"\"\"\n",
    "            :param x: (batch, seq_len, word_len)\n",
    "            :return: (batch, seq_len, char_channel_size)\n",
    "            \"\"\"\n",
    "            # x = (batch_sizes,seq_len,word_len)\n",
    "            batch_size = x.size(0)\n",
    "            x = self.dropout(self.char_emb(x))\n",
    "            # (batch, seq_len, word_len, char_dim)\n",
    "            x = x.view(-1, self.args.char_dim, x.size(2)).unsqueeze(1)\n",
    "            # (batch * seq_len, 1, char_dim, word_len) 1是输入的channel的维度\n",
    "            x = self.char_conv(x).squeeze()\n",
    "            # (batch * seq_len, char_channel_size, 1, conv_len) -> \n",
    "            # (batch * seq_len, char_channel_size, conv_len) conv_len不用管，下一步都会pool掉\n",
    "            x = F.max_pool1d(x, x.size(2)).squeeze()\n",
    "            # (batch * seq_len, char_channel_size, 1) -> (batch * seq_len, char_channel_size)\n",
    "            x = x.view(batch_size, -1, self.args.char_channel_size)\n",
    "            # (batch, seq_len, char_channel_size)\n",
    "\n",
    "            return x\n",
    "\n",
    "        def highway_network(x1, x2):\n",
    "            \"\"\"\n",
    "            :param x1: (batch, seq_len, char_channel_size)\n",
    "            :param x2: (batch, seq_len, word_dim)\n",
    "            :return: (batch, seq_len, hidden_size * 2)\n",
    "            \"\"\"\n",
    "            \n",
    "            x = torch.cat([x1, x2], dim=-1)\n",
    "            # x = (batch, seq_len, char_channel_size + word_dim)\n",
    "            for i in range(2):\n",
    "                h = getattr(self, f'highway_linear{i}')(x) # 调用Linear的forward方法\n",
    "                # h = (batch, seq_len, hidden_size * 2)\n",
    "                g = getattr(self, f'highway_gate{i}')(x)\n",
    "                # g = (batch, seq_len, hidden_size * 2)\n",
    "                x = g * h + (1 - g) * x\n",
    "            # (batch, seq_len, hidden_size * 2)\n",
    "            return x\n",
    "\n",
    "        def att_flow_layer(c, q):\n",
    "            \"\"\"\n",
    "            :param c: (batch, c_len, hidden_size * 2)\n",
    "            :param q: (batch, q_len, hidden_size * 2)\n",
    "            :return: (batch, c_len, q_len)\n",
    "            \"\"\"\n",
    "            c_len = c.size(1)\n",
    "            q_len = q.size(1)\n",
    "\n",
    "            # (batch, c_len, q_len, hidden_size * 2)\n",
    "            #c_tiled = c.unsqueeze(2).expand(-1, -1, q_len, -1)\n",
    "            # (batch, c_len, q_len, hidden_size * 2)\n",
    "            #q_tiled = q.unsqueeze(1).expand(-1, c_len, -1, -1)\n",
    "            # (batch, c_len, q_len, hidden_size * 2)\n",
    "            #cq_tiled = c_tiled * q_tiled\n",
    "            #cq_tiled = c.unsqueeze(2).expand(-1, -1, q_len, -1) * q.unsqueeze(1).expand(-1, c_len, -1, -1)\n",
    "#        # 4. Attention Flow Layer\n",
    "#         # 注意力层\n",
    "#         self.att_weight_c = Linear(args.hidden_size * 2, 1)\n",
    "#         self.att_weight_q = Linear(args.hidden_size * 2, 1)\n",
    "#         self.att_weight_cq = Linear(args.hidden_size * 2, 1)\n",
    "            cq = []\n",
    "            # 1、相似度计算方式，看下这篇博客理解：https://blog.csdn.net/u014665013/article/details/79793395\n",
    "            for i in range(q_len):\n",
    "                qi = q.select(1, i).unsqueeze(1)\n",
    "                # (batch, 1, hidden_size * 2)\n",
    "                # .select看这个：https://blog.csdn.net/hungryof/article/details/51802829\n",
    "                ci = self.att_weight_cq(c * qi).squeeze()\n",
    "                # (batch, c_len, 1)\n",
    "                cq.append(ci)\n",
    "            cq = torch.stack(cq, dim=-1) \n",
    "            # (batch, c_len, q_len) cp是共享相似度矩阵\n",
    "            \n",
    "            \n",
    "            # 2、计算对每一个 context word 而言哪些 query words 和它最相关。\n",
    "            # context-to-query attention(C2Q):\n",
    "            s = self.att_weight_c(c).expand(-1, -1, q_len) + \\\n",
    "                self.att_weight_q(q).permute(0, 2, 1).expand(-1, c_len, -1) + cq\n",
    "            # (batch, c_len, q_len) \n",
    "            a = F.softmax(s, dim=2) \n",
    "            # (batch, c_len, q_len)\n",
    "            c2q_att = torch.bmm(a, q) \n",
    "            # (batch, c_len, q_len) * (batch, q_len, hidden_size * 2) -> (batch, c_len, hidden_size * 2)\n",
    "            \n",
    "            \n",
    "            # 3、计算对每一个 query word 而言哪些 context words 和它最相关\n",
    "            # query-to-context attention(Q2C):\n",
    "            b = F.softmax(torch.max(s, dim=2)[0], dim=1).unsqueeze(1)\n",
    "            # (batch, 1, c_len)\n",
    "            q2c_att = torch.bmm(b, c).squeeze()\n",
    "            # (batch, 1, c_len) * (batch, c_len, hidden_size * 2) -> (batch, hidden_size * 2)\n",
    "            q2c_att = q2c_att.unsqueeze(1).expand(-1, c_len, -1)\n",
    "            # (batch, c_len, hidden_size * 2) (tiled)\n",
    "            # q2c_att = torch.stack([q2c_att] * c_len, dim=1)\n",
    "            \n",
    "            \n",
    "            # 4、最后将context embedding和C2Q、Q2C的结果（三个矩阵）拼接起来\n",
    "            x = torch.cat([c, c2q_att, c * c2q_att, c * q2c_att], dim=-1)\n",
    "            # (batch, c_len, hidden_size * 8)\n",
    "            return x\n",
    "\n",
    "        def output_layer(g, m, l):\n",
    "            \"\"\"\n",
    "            :param g: (batch, c_len, hidden_size * 8)\n",
    "            :param m: (batch, c_len ,hidden_size * 2)\n",
    "             #  l = c_lens\n",
    "            :return: p1: (batch, c_len), p2: (batch, c_len)\n",
    "            \"\"\"\n",
    "            p1 = (self.p1_weight_g(g) + self.p1_weight_m(m)).squeeze()\n",
    "            # (batch, c_len)\n",
    "            m2 = self.output_LSTM((m, l))[0]\n",
    "            # (batch, c_len, hidden_size * 2)\n",
    "            p2 = (self.p2_weight_g(g) + self.p2_weight_m(m2)).squeeze()\n",
    "            # (batch, c_len)\n",
    "            return p1, p2\n",
    "\n",
    "        # 1. Character Embedding Layer\n",
    "        # 令:一个batch中单词数量最多的样本长度为seq_len\n",
    "        # 令:一个batch中某个单词长度最长的单词长度为word_len\n",
    "        \n",
    "        c_char = char_emb_layer(batch.c_char) \n",
    "        # batch.c_char = (batch,seq_len,word_len) 后两个维度对应context\n",
    "        # c_char = (batch, seq_len, char_channel_size)\n",
    "\n",
    "        q_char = char_emb_layer(batch.q_char)\n",
    "        # batch.c_char = (batch,seq_len,word_len) 后两个维度对应question\n",
    "        # c_char = (batch, seq_len, char_channel_size)\n",
    "        \n",
    "        # 2. Word Embedding Layer\n",
    "        c_word = self.word_emb(batch.c_word[0])\n",
    "        # batch.c_word[0] = (batch,seq_len) 后一个维度对应context\n",
    "        # c_word = (batch, seq_len, word_dim) word_dim是Glove词向量维度\n",
    "        q_word = self.word_emb(batch.q_word[0]) \n",
    "        # batch.q_word[0] = (batch,seq_len) 后一个维度对应question\n",
    "        # q_word = (batch, seq_len, word_dim)\n",
    "        c_lens = batch.c_word[1]\n",
    "        # c_lens：一个batch中所有context的样本长度\n",
    "        q_lens = batch.q_word[1]\n",
    "        # q_lens：一个batch中所有question的样本长度\n",
    "\n",
    "        # Highway network\n",
    "        c = highway_network(c_char, c_word)\n",
    "        # c = (batch, seq_len, hidden_size * 2)\n",
    "        q = highway_network(q_char, q_word)\n",
    "        # q = (batch, seq_len, hidden_size * 2)\n",
    "        \n",
    "        # 3. Contextual Embedding Layer\n",
    "        c = self.context_LSTM((c, c_lens))[0]\n",
    "        # c = (batch, seq_len, hidden_size * 2)\n",
    "        q = self.context_LSTM((q, q_lens))[0]\n",
    "        # q = (batch, seq_len, hidden_size * 2)\n",
    "        \n",
    "        # 4. Attention Flow Layer\n",
    "        g = att_flow_layer(c, q)\n",
    "        # (batch, c_len, hidden_size * 8)\n",
    "        \n",
    "        # 5. Modeling Layer\n",
    "        m = self.modeling_LSTM2((self.modeling_LSTM1((g, c_lens))[0], c_lens))[0]\n",
    "        # self.modeling_LSTM1((g, c_lens))[0] = (batch, c_len, hidden_size * 2) # 2因为是双向\n",
    "        # m = (batch, c_len, hidden_size * 2) 2因为是双向\n",
    "        \n",
    "        # 6. Output Layer\n",
    "        p1, p2 = output_layer(g, m, c_lens) # 预测开始位置和结束位置\n",
    "        # (batch, c_len), (batch, c_len)\n",
    "        return p1, p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand((2,5,6))\n",
    "print(x)\n",
    "y = x.select(1, 2)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(data.WORD.vocab)) # 108777个单词\n",
    "print(data.WORD.vocab.vectors.shape) # 词向量维度\n",
    "\n",
    "print(data.WORD.vocab.itos[:50]) # 前50个词频最高的单词\n",
    "print(\"------\"*10)\n",
    "print(list(data.WORD.vocab.stoi.items())[0:50]) # 对应的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(data.CHAR.vocab)) # 1307个单词\n",
    "print(data.CHAR.vocab.itos[:50]) # 108777个单词\n",
    "print(\"------\"*10)\n",
    "print(list(data.CHAR.vocab.stoi.items())[0:50]) # 对应的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(f\"cuda:{args.gpu}\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = BiDAF(args, data.WORD.vocab.vectors).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EMA():\n",
    "    def __init__(self, mu):\n",
    "        # mu = args.exp_decay_rate = 0.999\n",
    "        self.mu = mu\n",
    "        self.shadow = {}\n",
    "\n",
    "    def register(self, name, val):\n",
    "        # name:各个参数层的名字, param.data；参数层的数据\n",
    "        self.shadow[name] = val.clone() # 建立字典\n",
    "        # clone()得到的Tensor不仅拷贝了原始的value，而且会计算梯度传播信息，copy_()只拷贝数值\n",
    "\n",
    "    def get(self, name):\n",
    "        return self.shadow[name]\n",
    "\n",
    "    def update(self, name, x):\n",
    "        assert name in self.shadow\n",
    "        new_average = (1.0 - self.mu) * x + self.mu * self.shadow[name]\n",
    "        self.shadow[name] = new_average.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, ema, args, data):\n",
    "    device = torch.device(f\"cuda:{args.gpu}\" if torch.cuda.is_available() else \"cpu\")\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    loss = 0\n",
    "    answers = dict()\n",
    "    model.eval()\n",
    "\n",
    "    backup_params = EMA(0)\n",
    "    for name, param in model.named_parameters():\n",
    "        if param.requires_grad:\n",
    "            backup_params.register(name, param.data) # 重新建立字典\n",
    "            param.data.copy_(ema.get(name))\n",
    "\n",
    "    with torch.set_grad_enabled(False):\n",
    "        for batch in iter(data.dev_iter):\n",
    "            p1, p2 = model(batch)\n",
    "            print(p1.shape,p2.shape)\n",
    "            print(batch.s_idx,batch.e_idx)\n",
    "            batch_loss = criterion(p1, batch.s_idx-1) + criterion(p2, batch.e_idx-1)\n",
    "            print(\"batch_loss\",batch_loss)\n",
    "            print(\"----\"*40)\n",
    "            loss += batch_loss.item()\n",
    "\n",
    "            # (batch, c_len, c_len)\n",
    "            batch_size, c_len = p1.size()\n",
    "            ls = nn.LogSoftmax(dim=1)\n",
    "            mask = (torch.ones(c_len, c_len) * float('-inf')).to(device).tril(-1).unsqueeze(0).expand(batch_size, -1, -1)\n",
    "            score = (ls(p1).unsqueeze(2) + ls(p2).unsqueeze(1)) + mask\n",
    "            score, s_idx = score.max(dim=1)\n",
    "            score, e_idx = score.max(dim=1)\n",
    "            s_idx = torch.gather(s_idx, 1, e_idx.view(-1, 1)).squeeze()\n",
    "\n",
    "            for i in range(batch_size):\n",
    "                id = batch.id[i]\n",
    "                answer = batch.c_word[0][i][s_idx[i]:e_idx[i]+1]\n",
    "                answer = ' '.join([data.WORD.vocab.itos[idx] for idx in answer])\n",
    "                answers[id] = answer\n",
    "\n",
    "        for name, param in model.named_parameters():\n",
    "            if param.requires_grad:\n",
    "                param.data.copy_(backup_params.get(name))\n",
    "\n",
    "    with open(args.prediction_file, 'w', encoding='utf-8') as f:\n",
    "        print(json.dumps(answers), file=f)\n",
    "\n",
    "    results = evaluate.main(args)\n",
    "    return loss, results['exact_match'], results['f1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    print(name)\n",
    "    print(param.requires_grad)\n",
    "    print(param.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strftime('%H:%M:%S', gmtime()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterator = data.train_iter\n",
    "n= 0\n",
    "for j in range(2):\n",
    "    print(\"j=\",j)\n",
    "    for i, batch in enumerate(iterator):\n",
    "        print(\"当前epoch\",int(iterator.epoch))\n",
    "        print(\"-----\"*10)\n",
    "        print(i)\n",
    "        print(batch)\n",
    "        n+=1\n",
    "        if n>3:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(args, data):\n",
    "    device = torch.device(f\"cuda:{args.gpu}\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model = BiDAF(args, data.WORD.vocab.vectors).to(device) # 定义主模型类实例\n",
    "\n",
    "    ema = EMA(args.exp_decay_rate) # args.exp_decay_rate = 0.999\n",
    "    for name, param in model.named_parameters(): \n",
    "        if param.requires_grad:\n",
    "            ema.register(name, param.data) # 参数名字和对应的参数数据形成字典\n",
    "    parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
    "    # p.requires_grad = True or False 保留有梯度的参数\n",
    "    optimizer = optim.Adadelta(parameters, lr=args.learning_rate)\n",
    "    # args.learning_rate = 0.5,优化器选用Adadelta\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    # 交叉熵损失\n",
    "\n",
    "    writer = SummaryWriter(log_dir='runs/' + args.model_time)\n",
    "    # args.model_time = strftime('%H:%M:%S', gmtime()) 文件夹命名为写入文件的当地时间\n",
    "\n",
    "    model.train()\n",
    "    loss, last_epoch = 0, -1\n",
    "    max_dev_exact, max_dev_f1 = -1, -1\n",
    "\n",
    "    iterator = data.train_iter\n",
    "    for i, batch in enumerate(iterator):\n",
    "        present_epoch = int(iterator.epoch) \n",
    "        #print(\"当前epoch\",present_epoch)# 这个我打印了下，一直是0，觉得有问题\n",
    "        if present_epoch == args.epoch:\n",
    "            # args.epoch=12\n",
    "            break\n",
    "        if present_epoch > last_epoch:\n",
    "            print('epoch:', present_epoch + 1)\n",
    "        last_epoch = present_epoch\n",
    "\n",
    "        p1, p2 = model(batch)\n",
    "        # (batch, c_len), (batch, c_len)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        batch_loss = criterion(p1, batch.s_idx) + criterion(p2, batch.e_idx)\n",
    "        # 最后的目标函数：batch.s_idx是答案开始的位置，batch.e_idx是答案结束的位置\n",
    "        loss += batch_loss.item()\n",
    "        batch_loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        for name, param in model.named_parameters():\n",
    "            if param.requires_grad:\n",
    "                ema.update(name, param.data) # 更新训练完后的的参数数据\n",
    "\n",
    "        if (i + 1) % args.print_freq == 0:\n",
    "            print(\"i\",i)\n",
    "            dev_loss, dev_exact, dev_f1 = test(model, ema, args, data)\n",
    "            c = (i + 1) // args.print_freq\n",
    "\n",
    "            writer.add_scalar('loss/train', loss, c)\n",
    "            writer.add_scalar('loss/dev', dev_loss, c)\n",
    "            writer.add_scalar('exact_match/dev', dev_exact, c)\n",
    "            writer.add_scalar('f1/dev', dev_f1, c)\n",
    "            print(f'train loss: {loss:.3f} / dev loss: {dev_loss:.3f}'\n",
    "                  f' / dev EM: {dev_exact:.3f} / dev F1: {dev_f1:.3f}')\n",
    "\n",
    "            if dev_f1 > max_dev_f1:\n",
    "                max_dev_f1 = dev_f1\n",
    "                max_dev_exact = dev_exact\n",
    "                best_model = copy.deepcopy(model)\n",
    "\n",
    "            loss = 0\n",
    "            model.train()\n",
    "\n",
    "    writer.close()\n",
    "#     print(f'max dev EM: {max_dev_exact:.3f} / max dev F1: {max_dev_f1:.3f}')\n",
    "\n",
    "    return best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('training start!')\n",
    "best_model = train(args, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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